1. Field of the Invention
The present invention is in the field of information technology and concerns cloud processing of medical data sets.
2. Description of the Prior Art
Particularly in the field of medical technology (radiology, for example) there are numerous use cases in which resource-intensive and memory-intensive image processing processes are outsourced to a cloud system. Since high-volume data sets are processed and/or transferred, particularly in the field of radiology, the available bandwidth is a constraint or constriction of the amount per time of data that can be transferred, and is thus known as a “bottleneck”. The advantage of the computing power and/or storage capacity provided by the cloud system cannot be used—or can be used only to a limited extent—when the transfer times for data exchange between the participating applications or clients and the cloud system are too high. For example, if a physician would like to deliberately process the radiological image data set to be assessed via a cloud system, but must wait too long for the result, the physician will not use this service in the future or will use this service only to a very limited extent.
In order to overcome this disadvantage, in the prior art it is known to avoid public networks or networks with too little bandwidth, and to integrate local cloud solutions—known as private clouds—directly into the application system (thus into radiology in the above example). The internal bandwidth of the network that is used can then be adapted upward. A significant disadvantage of such private cloud systems however, is that the actual advantages of a public cloud (for example nearly unlimited scalability) cannot be used, or can be used only to a limited extent. Furthermore, it is often not possible to install additional, faster data connections within an organization (hospital, for example). Even if a new installation of network connections should be possible, this is disadvantageously associated with high costs.
Therefore, an additional known approach in the prior art is to not shift data-intensive processing algorithms and processes into the cloud, but rather to execute them at internal, locally present computer systems that have been set up for this purpose with regard to storage capacity and computing power. This is also associated with economic disadvantages.
However, if the user of current systems would prefer not to forego the advantages of cloud computing, the internal and external network connections to the cloud and from the cloud are the “chokepoint” that leads to long transfer times.
Furthermore, in the prior art it is known to use compression methods in order to transfer data in compressed form (see for example JPEG2000 or progressive JPEG, or compression formats such as ZIP or the like). However, these systems can disadvantageously be applied only to a limited extent to the computer-based data formats that are currently in use in medical technology—in particular in radiology (DICOM format)—due to the disadvantages that are connected with these formats. Data that are transferred into the cloud have thus previously been compressed at the sender with known compression methods (ZIP, for example) and decompressed at the receiver before they are supplied to the cloud computer together with the algorithm to be used for processing the data. This leads to a significant time delay at the sender and at the receiver.